Emissions of greenhouse gases from ponds constructed for nitrogen removal

Methane and carbon dioxide emission from three constructed ponds were monitored during an annual cycle. Water temperature was a good predictor of methane emission in all three ponds. In the most intensively studied pond, nitrate concentration in the bottom water could further explain the amount of methane emitted. When water temperature exceeded 15 °C between 1 and 54 mg, CH₄ m⁻² h⁻¹ was emitted on all occasions, while at temperatures below 10 °C, less than 0.6 mg CH₄ m⁻² h⁻¹ was emitted. The flux of carbon dioxide differed between the ponds and no consistent patterns were found. In a laboratory study at 20 °C, we showed that high, but naturally occurring, nitrate concentrations (8 and 16 mg NO₃⁻–N l⁻¹) constrained the production of methane compared to the treatment with no nitrate addition. Nitrous oxide production was positively correlated with nitrate concentration. Carbon dioxide production was highest at the highest nitrate concentration, which indicates that increased nitrate loading on ponds and wetlands will stimulate organic matter decomposition rates. Our conclusion is that these ponds constructed for nitrate removal emit greenhouse gases comparable to lakes in the temperate region.     

Implications of floristic and environmental variation for carbon cycle dynamics in boreal forest ecosystems of central Canada

Abstract. Species composition, detritus, and soil data from 97 boreal forest stands along a transect in central Canada were analysed using Correspondence Analysis to determine the dominant environmental/site variables that differentiate these forest stands. Picea mariana stands were densely clustered together on the understorey DCA plot, suggesting a consistent understorey species composition (feather mosses and Ericaceae), whereas Populus tremuloides stands had the most diverse understorey species composition (ca. 30 species, mostly shrubs and herbs). Pinus banksiana stands had several characteristic species of reindeer lichens (Cladina spp.), but saplings and Pinus seedlings were rare. Although climatic variables showed large variation along the transect, the CCA results indicated that site conditions are more important in determining species composition and differentiating the stand types. Forest floor characteristics (litter and humus layer, woody debris, and drainage) appear to be among the most important site variables. Stands of Picea had significantly higher average carbon (C) densities in the combined litter and humus layer (43530 kg‐C.ha^‐1) than either Populus (25 500 kg‐C.ha^‐1) or Pinus (19 400 kg‐C.ha^‐1). The thick surface organic layer in lowland Picea stands plays an important role in regulating soil temperature and moisture, and organic‐matter decomposition, which in turn affect the ecosystem C‐dynamics. During forest succession after a stand‐replacing disturbance (e.g. fires), tree biomass and surface organic layer thickness increase in all stand types as forests recover; however, woody biomass detritus first decreases and then increases after ca. 80 yr. Soil C densities show slight decrease with ages in Populus stands, but increase in other stand types. These results indicate the complex C‐transfer processes among different components (tree biomass, detritus, forest floor, and soil) of boreal ecosystems at various stages of succession.     

Revising a process-based biogeochemistry model (DNDC) to simulate methane emission from rice paddy fields under various residue management and fertilizer regimes

A comprehensive biogeochemistry model, DNDC, was revised to simulate crop growth and soil processes more explicitly and improve its ability to estimate methane (CH₄) emission from rice paddy fields under a wide range of climatic and agronomic conditions. The revised model simulates rice growth by tracking photosynthesis, respiration, C allocation, tillering, and release of organic C and O₂ from roots. For anaerobic soil processes, it quantifies the production of electron donors [H₂ and dissolved organic carbon (DOC)] by decomposition and rice root exudation, and simulates CH₄ production and other reductive reactions based on the availability of electron donors and acceptors (NO₃^?, Mn⁴⁺, Fe³⁺, and SO₄^2?). Methane emission through rice is simulated by a diffusion routine based on the conductance of tillers and the CH₄ concentration in soil water. The revised DNDC was tested against observations at three rice paddy sites in Japan and China with varying rice residue management and fertilization, and produced estimates consistent with observations for the variation in CH₄ emission as a function of residue management. It also successfully predicted the negative effect of (NH₄)₂SO₄ on CH₄ emission, which the current model missed. Predicted CH₄ emission was highly sensitive to the content of reducible soil Fe³⁺, which is the dominant electron acceptor in anaerobic soils. The revised DNDC generally gave acceptable predictions of seasonal CH₄ emission, but not of daily CH₄ fluxes, suggesting the model's immaturity in describing soil heterogeneity or rice cultivar‐specific characteristics of CH₄ transport. It also overestimated CH₄ emission at one site in a year with low temperatures, suggesting uncertainty in root biomass estimates due to the model's failure to consider the temperature dependence of leaf area development. Nevertheless, the revised DNDC explicitly reflects the effects of soil electron donors and acceptors, and can be used to quantitatively estimate CH₄ emissions from rice fields under a range of conditions.     

Analyzing the ecosystem carbon and hydrologic characteristics of forested wetland using a biogeochemical process model

A comprehensive biogeochemical model, Wetland‐DNDC, was applied to analyze the carbon and hydrologic characteristics of forested wetland ecosystem at Minnesota (MN) and Florida (FL) sites. The model simulates the flows of carbon, energy, and water in forested wetlands. Modeled carbon dynamics depends on physiological plant factors, the size of plant pools, environmental factors, and the total amount and turnover rates of soil organic matter. The model realistically simulated water level fluctuation, forest production, carbon pools change, and CO₂ and CH₄ emission under natural variations in different environmental factors at two sites. Analyses were focused on parameters and inputs potentially cause the greatest uncertainty in calculated change in plant and soil C and water levels fluctuation and shows that it was important to obtain accurate input data for initial C content, climatic conditions, and allocation of net primary production to various forested wetland components. The magnitude of the forest responses was dependent not only on the rate of changes in environmental factors, but also on site‐specific conditions such as climate and soil. This paper explores the ability of using the biogeochemical process model Wetland‐DNDC to estimate the carbon and hydrologic dynamics of forested wetlands and shifts in these dynamics in response to changing environmental conditions.     

Studies on the dynamic properties of terrestrial ecosystems based on a simulation model II. Tropical rainforest dynamics and stability as influenced by stem mortality

Further analysis of tropical rainforest dynamics and stability in relation to stem mortality has been conducted using a microcomputer model developed in a previous study (Oikawa, 1985). By simulation experiments covering a period of 100 years, the effects of changing stem mortality (δc) upon a tropical rainforest were investigated. Increasing stem mortality ranging from a standard value (3%yr⁻¹) to a 4-fold value (12%yr⁻¹) brings about decreases in stem biomass and thus total living biomass, and a contrasting increase of stem litterfall flux at the steady state of the forest ecosystem. At the same time, the decreased stem biomass at the steady state is predicted to result in increases of gross production (P _g) and net production (P _n), and an improvement in production efficiency of the model rainforest expressed as theP _n/P_g ratio. similar simulation experiments predict that the improved production efficiency in the forest with a 4-fold stem mortality is able to enhance tolerance to less productive environments such as a prolonged dry season or a reduced incident light flux density. On the other hand, the standard stem mortality (δ_c=3%yr⁻¹), which was estimated as a probable value for the Pasoh forest, West Malaysia, is considered to approximate the lower threshold necessary for attaining forest stability. Based on the results obtained, the significance of δ_c for the dynamics and stability of a tropical rainforest ecosystem is discussed in relation to the competition and tolerance of trees. In addition, the effectiveness of the simulation approach adopted here is emphasized. Titles are tentative translations by the author for original titles in Japanese.     

Control of carbon mineralization to CH4 and CO2 in anaerobic,Sphagnum-derived peat from Big Run Bog,West Virginia

The mineralization of organic carbon to CH₄ and CO₂ inSphagnum-derived peat from Big Run Bog, West Virginia, was measured at 4 times in the year (February, May, September, and November) using anaerobic, peat-slurry incubations. Rates of both CH₄ production and CO₂ production changed seasonally in surface peat (0–25 cm depth), but were the same on each collection date in deep peat (30–45 cm depth). Methane production in surface peat ranged from 0.2 to 18.8 mol mol(C)^–1 hr^–1 (or 0.07 to 10.4 g(CH₄) g^–1 hr^–1) between the February and September collections, respectively, and was approximately 1 mol mol(C)^–1 hr^–1 in deep peat. Carbon dioxide production in surface peat ranged from 3.2 to 20 mol mol(C)^–1 hr^–1 (or 4.8 to 30.3 g(CO₂) g^–1 hr^–1) between the February and September collections, respectively, and was about 4 mol mol(C)^–1 hr^–1 in deep peat. In surface peat, temperature the master variable controlling the seasonal pattern in CO₂ production, but the rate of CH₄ production still had the lowest values in the February collection even when the peat was incubated at 19°C. The addition of glucose, acetate, and H₂ to the peat-slurry did not stimulate CH₄ production in surface peat, indicating that CH₄ production in the winter was limited by factors other than glucose degradation products. The low rate of carbon mineralization in deep peat was due, in part, to poor chemical quality of the peat, because adding glucose and hydrogen directly stimulated CH₄ production, and CO₂ production to a lesser extent. Acetate was utilized in the peat by methanogens, but became a toxin at low pH values. The addition of SO₄ ^2– to the peat-slurry inhibited CH4 production in surface peat, as expected, but surprisingly increased carbon mineralization through CH4 production in deep peat. Carbon mineralization under anaerobic conditions is of sufficient magnitude to have a major influence on peat accumulation and helps to explain the thin (< 2 m deep), old (> 13,000 yr) peat deposit found in Big Run Bog.     

Calculation of diffusion coefficient for supercritical carbon dioxide and carbon dioxide+naphthalene system by molecular dynamics simulation using EPM2 model

NVT ensemble molecular dynamics (MD) simulation has been applied to calculate the self-diffusion coefficients of carbon dioxide and the tracer diffusion coefficients of naphthalene in supercritical carbon dioxide. The simulation was carried out in the pressure range from 8 to 40 MPa. The elementary physical model proposed by Harris and Yung was adopted for carbon dioxide and some approximation models were used for naphthalene. The systems of MD simulation for carbon dioxide consist of 256 particles. One naphthalene molecule was added for carbon dioxide+naphthalene system. The system can be assumed to be an infinite dilution condition for carbon dioxide+naphthalene system and the mutual diffusion coefficients are equal to the tracer diffusion coefficients of naphthalene. The self-diffusion coefficients of carbon dioxide and the tracer diffusion coefficients of naphthalene in supercritical carbon dioxide can be calculated by mean square displacement. The calculated results of diffusion coefficients showed good agreement with the experimental data without adjustable parameters.     

Contribution of muscle short-range stiffness to initial changes in joint kinetics and kinematics during perturbations to standing balance: A simulation study

Simulating realistic musculoskeletal dynamics is critical to understanding neural control of muscle activity evoked in sensorimotor feedback responses that have inherent neural transmission delays. Thus, the initial mechanical response of muscles to perturbations in the absence of any change in muscle activity determines which corrective neural responses are required to stabilize body posture. Muscle short-range stiffness, a history-dependent property of muscle that causes a rapid and transient rise in muscle force upon stretch, likely affects musculoskeletal dynamics in the initial mechanical response to perturbations. Here we identified the contributions of short-range stiffness to joint torques and angles in the initial mechanical response to support surface translations using dynamic simulation. We developed a dynamic model of muscle short-range stiffness to augment a Hill-type muscle model. Our simulations show that short-range stiffness can provide stability against external perturbations during the neuromechanical response delay. Assuming constant muscle activation during the initial mechanical response, including muscle short-range stiffness was necessary to account for the rapid rise in experimental sagittal plane knee and hip joint torques that occurs simultaneously with very small changes in joint angles and reduced root mean square errors between simulated and experimental torques by 56% and 47%, respectively. Moreover, forward simulations lacking short-range stiffness produced unreasonably large joint angle changes during the initial response. Using muscle models accounting for short-range stiffness along with other aspects of history-dependent muscle dynamics may be important to advance our ability to simulate inherently unstable human movements based on principles of neural control and biomechanics.     

Molecular simulation of carbon nanotubes as sorptive materials: sorption effects towards retene,perylene and cholesterol to 100 degrees Celsius and above

New water purification technologies are being developed as the world’s water sources are increasingly being polluted and experience a dramatic consumption with the increasing world population. In this context, the emerging era of nanotechnology has introduced a series of innovations and materials with promising potential as sorptive materials for water decontamination. The application of nanomaterials for the purification of ground/surface water introduces nevertheless a series of important challenges, such as health and safety, cost, process-efficiency and chemical sorption properties. In this study, we consider the latter class and present a study of the sorptive properties of carbon nanotubes (CNTs) as potential water decontaminating materials. Molecular dynamics simulations are used and three molecular candidates of the water contaminants, cholesterol, perylene and retene were selected for interaction study with CNTs at different diameters. The results show that CNTs form densely packed clusters with retene, perylene and cholesterol, binding each strongly to their tubular surfaces, as well as in their hollow tubular spaces. Cholesterol and perylene bind more strongly than retene, accounting for the calculated binding energies in vacuo, however the planar geometries of polycyclics may in general favour binding to CNTs over semi-polar molecules and can require further studies. Our studies show furthermore that the CNTs retain the adsorbed molecules also at 100 degrees Celsius, and require therefore additional steps of separation for eventual recycling and reusing the nanomaterials for additional decontamination. This study is important in providing data for initiating studies and developments of water purification approaches based on using CNTs.     

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate‐change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process‐oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large‐scale remote‐sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate‐change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.     

Tidal downwelling and implications for the carbon biogeochemistry of cold‐water corals in relation to future ocean acidification and warming

Cold‐water coral (CWC) reefs are recognized as ecologically and biologically significant areas that generate habitats and diversity. The interaction between hydrodynamics and CWCs has been well studied at the Mingulay Reef Complex, a relatively shallow area of reefs found on the continental shelf off Scotland, UK. Within ‘Mingulay Area 01’ a rapid tidal downwelling of surface waters, brought about as an internal wave, is known to supply warmer, phytoplankton‐rich waters to corals growing on the northern flank of an east‐west trending seabed ridge. This study shows that this tidal downwelling also causes short‐term perturbations in the inorganic carbon (C_T) and nutrient dynamics through the water column and immediately above the reef. Over a 14 h period, corresponding to one semi‐diurnal tidal cycle, seawater pH overlying the reef varied by ca. 0.1 pH unit, while pCO₂ shifted by >60 μatm, a shift equivalent to a ca. 25 year jump into the future, with respect to atmospheric pCO₂. During the summer stratified period, these downwelling events result in the reef being washed over with surface water that has higher pH, is warmer, nutrient depleted, but rich in phytoplankton‐derived particles compared to the deeper waters in which the corals sit. Empirical observations, together with outputs from the European Regional Shelf Sea Ecosystem Model, demonstrate that the variability that the CWC reefs experience changes through the seasons and into the future. Hence, as ocean acidification and warming increase into the future, the downwelling event specific to this site could provide short‐term amelioration of corrosive conditions at certain times of the year; however, it could additionally result in enhanced detrimental impacts of warming on CWCs. Natural variability in the C_T and nutrient conditions, as well as local hydrodynamic regimes, must be accounted for in any future predictions concerning the responses of marine ecosystems to climate change.     

A comparison of field methods for measuring soil carbon dioxide evolution: Experiments and simulation

Three widely used methods for measuring total soil CO₂ evolution are evaluated, including the dynamic CO₂ absorption method, the static CO₂ absorption method and the closed chamber method. The study covers laboratory experiments. numerical experiments with a simulation model and field measurements. The results are used to perform an error analysis. The aim of this error analysis is to indicate the impact of each method on the CO₂ dynamics during the measurement, and to select the most suitable method for frequent field usage.Laboratory experiments and simulation results show that the dynamic CO₂ absorption method has the potential to absorb all CO₂ evolving at the soil surface. The results also prove that the method has only a minor impact on the CO₂ concentration-depth gradient and the CO₂ efflux. The static CO₂ absorption method underestimates the soil CO₂ evolution, because the absorption velocity is too low, due to slow diffusion processes. Measurements with the closed-chamber method are based on an increasing concentration with time under a closed cover. However, the accumulation of gas alters the concentration gradient in the soil profile and thus causes a rapidly decreasing efflux during the measurement. A commonly used mathematical procedure, which corrects for the altered concentration gradient, does not yield the exact surface efflux, because the effect of increasing storage in the soil profile is not incorporated. Field measurements of CO₂ evolution, using the closed-chamber method and the dynamic CO₂ absorption method confirm the trends that have been predicted by the simulation model. The results of this study indicate that the dynamic CO₂ absorption method is accurate. As it is cheap and simple, it is suitable for the study of temporal and spatial dynamics of CO₂ evolution from the soil.     

Productivity and vitality of spruce stands: dynamic feedback simulation for responses to different annual and seasonal levels of magnesium supply from soil

Responses of Norway spruce stands to gradually decreased annual magnesium supply from soil, with two seasonal courses, are evaluated by computer simulation. At low supply, magnesium concentrations in the needles are reduced to levels inducing chlorosis, but annual growth of new needles remains at the same level as stands with adequate magnesium. Above-ground permanent biomass shows a decreased annual production with lower supply, mainly because of shorter net carbon gain caused by decreased assimilation which is due to magnesium deficiency in needles. An additional but less important reason is the magnesium deficiency in stem and branch cambium, which is most severe during summer at low magnesium supply rates. The interaction of magnesium and carbon balance is emphasized.Modeling and simulation are not used for prediction but to test the consistency of scientific hypotheses.     

Homology modeling and molecular dynamics simulation of odonthubuthus doriae (Od1) scorpion toxin in comparison to the BmK M1

All of the α-subgroups share similarity in their sequence and structure but different in the toxicity to various voltage-gated sodium channels (VGSCs). We modeled the first 3D structural model of the Od1 based on BmK M1 using homology modeling. The reliability of model for more investigation and compare to BmK M1 has been examined and confirmed. Then the model structure is further refined by energy minimization and molecular dynamics methods. The purpose of this modeling and simulation is comparison toxicity of two mentioned toxins by investigation structural feature of functional regions including core domain, 5-turn and C-terminal which make NC domain. In the one hand, it is intriguing that Od1 in comparison to BmK M1 shows same solvent accessible surface area (SASA) in 5-turn region but a little more exposed and feasibility (more SASA) in C-terminal region and key functional residues of C-terminal such as positive residues Arg58, lys62 and Arg (His)64. These data suggested that Od1 has similarity with BmK M1 but has more toxicity to sodium channel. In the other hand 5-turn proximity of C-terminal to 5-turn in BmK M1with cis peptide bond is less than Od1 without cis peptide bond which is a confirmation with experimental data about BmK M1.A better understanding of the 3-D structure of Od1and comparison to BmK M1 will be helpful for more investigation of functional characters action of natural toxins with a specialized role for VGSCs.     

Expansion of the scaffold diversity for the development of highly selective butyrylcholinesterase (BChE) inhibitors: Discovery of new hits through the pharmacophore model generation,virtual screening and molecular dynamics simulation

Butyrylcholinesterase (BChE) is recently considered as a new target for the treatment of Alzheimer’s disease (AD). There is an increasing interest in the development of BChE inhibitors. In the present study, a set of pharmacophore models for BChE was developed and validated. Based on the models, virtual screening was performed on five compound collections, from which seventeen potential hits were retained for biological investigation. In total, eight of these seventeen potential hits showed selective BChE inhibitory activity. Moreover, four compounds displayed IC₅₀ values in sub-micromolar range on eqBChE and three displayed IC₅₀ values < 2 μM on huBChE. The diverse scaffolds of the active compounds provided good starting point further development of selective BChE inhibitors. As far as we concerned, here we disclose the first selective pharmacophore model targeting BChE. The high rate of the model in the identification of active hits indicates it is a valuable tool for the development of selective BChE inhibitors, which may benefit the treatment of AD.     

In silico high-throughput virtual screening and molecular dynamics simulation study to identify inhibitor for AdeABC efflux pump of Acinetobacter baumannii

Emergence of multi-drug resistant strains of Acinetobacter baumannii has caused significant health problems and is responsible for high morbidity and mortality. Overexpression of AdeABC efflux system is one of the major mechanisms. In this study, we have focused on overcoming the drug resistance by identifying inhibitors that can effectively bind and inhibit integral membrane protein, AdeB of this efflux pump. We performed homology modeling to generate structure of AdeB using MODELLER v9.16 followed by model refinement using 3D-Refine tool and validated using PSVS, ProsaWeb, ERRAT, etc. The energy minimization of modeled protein was done using Protein preparation wizard application included in Schrodinger suite. High-throughput virtual screening of 159,868 medicinal compounds against AdeB was performed using three sequential docking modes (i.e. HTVS, SP and XP). Furthermore, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis was done using QIKPROP. The selected 123 compounds were further analyzed for binding free energy by molecular mechanics (using prime MM-GBSA). We have also performed enrichment study (ROC curve analysis) to validate our docking results. The selected molecule and its interaction with AdeB were validated by molecular dynamics simulation (MDS) using GROMACS v5.1.4. In silico high-throughput virtual screening and MDS validation identified ZINC01155930 ((4R)-3-(cycloheptoxycarbonyl)-4-(4-etochromen-3-yl)-2-methyl-4,6,7,8-tetrahydroquinolin-5-olate) as a possible inhibitor for AdeB. Hence, it might be a suitable efflux pump inhibitor worthy of further investigation in order to be used for controlling infections caused by Acinetobacter baumannii.     

Responses of dryland soil respiration and soil carbon pool size to abrupt vs. gradual and individual vs. combined changes in soil temperature, precipitation, and atmospheric [CO2]: a simulation analysis

With the large extent and great amount of soil carbon (C) storage, drylands play an important role in terrestrial C balance and feedbacks to climate change. Yet, how dryland soils respond to gradual and concomitant changes in multiple global change drivers [e.g., temperature (T_s), precipitation (Ppt), and atmospheric [CO₂] (CO₂)] has rarely been studied. We used a process‐based ecosystem model patch arid land simulator to simulate dryland soil respiration (R_s) and C pool size (C_s) changes to abrupt vs. gradual and single vs. combined alterations in T_s, Ppt and CO₂ at multiple treatment levels. Results showed that abrupt perturbations generally resulted in larger R_s and had longer differentiated impacts than did gradual perturbations. R_s was stimulated by increases in T_s, Ppt, and CO₂ in a nonlinear fashion (e.g., parabolically or asymptotically) but suppressed by Ppt reduction. Warming mainly stimulated heterotrophic R_s (i.e., R_h) whereas Ppt and CO₂ influenced autotrophic R_s (i.e., R_a). The combined effects of warming, Ppt, and CO₂ were nonadditive of primary single‐factor effects as a result of substantial interactions among these factors. Warming amplified the effects of both Ppt addition and CO₂ elevation whereas Ppt addition and CO₂ elevation counteracted with each other. Precipitation reduction either magnified or suppressed warming and CO₂ effects, depending on the magnitude of factor's alteration and the components of R_s (R_a or R_h) being examined. Overall, Ppt had dominant influence on dryland R_s and C_s over T_s and CO₂. Increasing Ppt individually or in combination with T_s and CO₂ benefited soil C sequestration. We therefore suggested that global change experimental studies for dryland ecosystems should focus more on the effects of precipitation regime changes and the combined effects of Ppt with other global change factors (e.g., T_s, CO₂, and N deposition).     

Responses to atmospheric CO2 concentrations in crop simulation models: a review of current simple and semicomplex representations and options for model development

Elevated atmospheric CO₂ concentrations ([CO₂]) cause direct changes in crop physiological processes (e.g. photosynthesis and stomatal conductance). To represent these CO₂ responses, commonly used crop simulation models have been amended, using simple and semicomplex representations of the processes involved. Yet, there is no standard approach to and often poor documentation of these developments. This study used a bottom‐up approach (starting with the APSIM framework as case study) to evaluate modelled responses in a consortium of commonly used crop models and illuminate whether variation in responses reflects true uncertainty in our understanding compared to arbitrary choices of model developers. Diversity in simulated CO₂ responses and limited validation were common among models, both within the APSIM framework and more generally. Whereas production responses show some consistency up to moderately high [CO₂] (around 700 ppm), transpiration and stomatal responses vary more widely in nature and magnitude (e.g. a decrease in stomatal conductance varying between 35% and 90% among models was found for [CO₂] doubling to 700 ppm). Most notably, nitrogen responses were found to be included in few crop models despite being commonly observed and critical for the simulation of photosynthetic acclimation, crop nutritional quality and carbon allocation. We suggest harmonization and consideration of more mechanistic concepts in particular subroutines, for example, for the simulation of N dynamics, as a way to improve our predictive understanding of CO₂ responses and capture secondary processes. Intercomparison studies could assist in this aim, provided that they go beyond simple output comparison and explicitly identify the representations and assumptions that are causal for intermodel differences. Additionally, validation and proper documentation of the representation of CO₂ responses within models should be prioritized.     

A comparison of the multilevel MIMIC model to the multilevel regression and mixed ANOVA model for the estimation and testing of a cross-level interaction effect: A simulation study

When observing data on a patient-reported outcome measure in, for example, clinical trials, the variables observed are often correlated and intended to measure a latent variable. In addition, such data are also often characterized by a hierarchical structure, meaning that the outcome is repeatedly measured within patients. To analyze such data, it is important to use an appropriate statistical model, such as structural equation modeling (SEM). However, researchers may rely on simpler statistical models that are applied to an aggregated data structure. For example, correlated variables are combined into one sum score that approximates a latent variable. This may have implications when, for example, the sum score consists of indicators that relate differently to the latent variable being measured. This study compares three models that can be applied to analyze such data: the multilevel multiple indicators multiple causes (ML-MIMIC) model, a univariate multilevel model, and a mixed analysis of variance (ANOVA) model. The focus is on the estimation of a cross-level interaction effect that presents the difference over time on the patient-reported outcome between two treatment groups. The ML-MIMIC model is an SEM-type model that considers the relationship between the indicators and the latent variable in a multilevel setting, whereas the univariate multilevel and mixed ANOVA model rely on sum scores to approximate the latent variable. In addition, the mixed ANOVA model uses aggregated second-level means as outcome. This study showed that the ML-MIMIC model produced unbiased cross-level interaction effect estimates when the relationships between the indicators and the latent variable being measured varied across indicators. In contrast, under similar conditions, the univariate multilevel and mixed ANOVA model underestimated the cross-level interaction effect.